# Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field

arXiv: https://arxiv.org/abs/2212.04329 · HTML: https://mlsanity.com/paper/2212.04329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 353 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 27 of 27)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [One-Class Adversarial Nets for Fraud Detection](https://mlsanity.com/paper/1803.01798/) (2018)
- [A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends](https://mlsanity.com/paper/1905.13294/) (2019)
- [Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics](https://mlsanity.com/paper/2004.01509/) (2020)
- [Helen: Maliciously Secure Coopetitive Learning for Linear Models](https://mlsanity.com/paper/1907.07212/) (2019)
- [Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection](https://mlsanity.com/paper/1804.07091/) (2018)
- [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018)
- [SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark](https://mlsanity.com/paper/1709.08920/) (2017)
- [The Journal Coverage of Web of Science and Scopus: a Comparative Analysis](https://mlsanity.com/paper/1511.08096/) (2015)
- [Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization](https://mlsanity.com/paper/1804.07481/) (2018)
- [Using social network analysis to prevent money laundering](https://mlsanity.com/paper/2105.05793/) (2021)
- [Kernel density estimation based sampling for imbalanced class distribution](https://mlsanity.com/paper/1910.07842/) (2019)
- [Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection](https://mlsanity.com/paper/2201.01004/) (2022)
- [RePAD: Real-time Proactive Anomaly Detection for Time Series](https://mlsanity.com/paper/2001.08922/) (2020)
- [HoloScope: Topology-and-Spike Aware Fraud Detection](https://mlsanity.com/paper/1705.02505/) (2017)
- [Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs](https://mlsanity.com/paper/1909.01185/) (2019)
- [No Place to Hide: Catching Fraudulent Entities in Tensors](https://mlsanity.com/paper/1810.06230/) (2018)
- [Gamma distribution-based sampling for imbalanced data](https://mlsanity.com/paper/2009.10343/) (2020)
- [BIRDNEST: Bayesian Inference for Ratings-Fraud Detection](https://mlsanity.com/paper/1511.06030/) (2015)
- [Knowledge Sharing via Domain Adaptation in Customs Fraud Detection](https://mlsanity.com/paper/2201.06759/) (2022)
- [Temporal Data Exchange](https://mlsanity.com/paper/1609.06526/) (2016)
- [Technical Report: Accelerating Dynamic Graph Analytics on GPUs](https://mlsanity.com/paper/1709.05061/) (2017)

## Built on by (most cited first, 1 of 1)

- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)

## Abstract (arXiv)

The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall little organization of the many aspects of fraud analytical research. The focus of academics ranges from identifying fraudulent credit card payments to spotting illegitimate insurance claims. In addition, there is a wide range of methods and research objectives. This paper aims to provide an overview of fraud analytics in research and aims to more narrowly organize the discipline and its many subfields. We analyze a sample of almost 300 records on fraud analytics published between 2011 and 2020. In a systematic way, we identify the most prominent domains of application, challenges faced, performance metrics, and methods used. In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research. One of the key challenges in fraud analytics is access to public datasets. To further aid the community, we provide eight requirements for suitable data sets in research motivated by our research. We structure our sample of the literature in an online database. The database is available online for fellow researchers to investigate and potentially build upon.